Weight bias reduction among first‐year medical students: A quasi‐randomized, controlled trial. Issue 6 (14th July 2021)

• Record Type: Journal Article
• Authors: Fitterman‐Harris, Hannah F.; Vander Wal, Jillon S.
• Is Part Of: Clinical obesity. Volume 11:Issue 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-14
• Subjects: medical students -- obesity -- stigma -- weight bias; Obesity -- Periodicals; Obesity -- Treatment -- Periodicals; 616.398005
• DOI: 10.1111/cob.12479
• Languages: English
• ISSNs: 1758-8103

Abstract

Summary: Implicit and explicit weight bias is prevalent among healthcare professionals and results in negative outcomes, including weak physician‐patient rapport, low patient trust in physicians, and avoidance of healthcare. This study aimed to decrease weight bias in medical students via a one‐session, curriculum‐based intervention founded on the elaboration likelihood model (ELM). First‐year medical students ( N = 101) were quasi‐randomly assigned to a group‐based experimental or education‐based control intervention. Data collected included measures of implicit and explicit weight bias and empathy (pre‐intervention and up to 2 days post‐intervention), and thoughts regarding weight bias (collected at the end of the intervention). Social desirability was identified as an important covariate. After controlling for social desirability, the intervention group showed no greater reduction in multi‐item explicit bias rating scales or a computerized implicit bias task than the control group. Both groups showed less discomfort when near individuals with obesity ( p < 0.01). The intervention group wrote a greater number of statements about weight bias and obesity ( p < 0.05) and a greater number of statements about the harms of weight bias ( p < 0.001) than the control group on a thought‐listing task. Results highlight use of the ELM and the importance of controlling for social desirability in weight bias interventions. A thought‐listing task captured unique intervention effects, highlighting potential gaps in the assessment of weight bias.